Machine-Learning

October 30, 2018 · View on GitHub

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Chapt. 2: K-Nearest Neighbors Algorithm

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Python3 study notes (I): K-Nearest Neighbors Algorithm (Gorgeous and Splendid Tutorial)Personal WebsiteCSDNZhihu

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Chapt. 3: Decision Tree

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Python3 study notes (II): Decision Tree (Basic Concepts): Let's Start from DatingPersonal WebsiteCSDNZhihu
Python3 study notes (III): Decision Tree (In Practice): I'm Looking for a Pair of Contact LensesPersonal WebsiteCSDNZhihu

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Chapt. 4: Navie Bayes

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Python3 Python3 study notes (IV): Navie Bayes (Basic Concepts): Comment FilterPersonal WebsiteCSDNZhihu
Python3 Python3 study notes (V): Navie Bayes (In Practice): Catalogues of Sina NewsPersonal WebsiteCSDNZhihu

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Chapt. 5: Logistic Regression

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Python3 study notes (VI): Logistic Regression (Basic Concepts): Gradient Ascent AlgorithmPersonal WebsiteCSDNZhihu
Python3 study notes (VII): Logistic Regression (In Practice): Prediction of Horse MortalityPersonal WebsiteCSDNZhihu

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Chapt. 6: SVM (Support Vector Machine)

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Python3 study notes (VIII): SVM (Basic Concepts): a Handcraft on Linear SVMPersonal WebsiteCSDNZhihu
Python3 study notes (IX): SVM (In Practice): Another Handcraft on Nonlinear SVMPersonal WebsiteCSDNZhihu

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Chapt. 7: AdaBoost

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Python3 study notes (X): Classifier Sharpener -- AdaBoostPersonal WebsiteCSDNZhihu

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Chapt. 8: Linear Regression

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Python3 study notes (XI):Personal WebsiteCSDNZhihu
Python3 study notes (XII):Personal Websitenono

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Chapt. 9: Regression Tree

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Python3 study notes (XIII): Regression Tree (Basic Concepts): CART Alogrithm and PruningPersonal Websitenono

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